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Privacy-Preserving Computing in the Music Industry

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Yulu Wang
Charlotte van de Velde
Sabine Oechsner
Jaap Gordijn
Published in
The Practice of Enterprise Modeling
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the application of privacy-preserving computation (PPC) techniques in the music industry, focusing on the MUSIC360 project. It explores the need for advanced PPC methods like Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and Differential Privacy (DP) to protect sensitive data while allowing for collaborative computations. The text outlines a systematic approach to selecting and integrating these technologies, addressing the challenges and limitations of current solutions. A case study in the music industry demonstrates the practical application of these techniques, showing how they can be used to calculate the average revenue of venues without disclosing individual data. The chapter also discusses the importance of a decision-making framework that bridges technical PPC features with real-world requirements, validated through a proof-of-concept experiment. This comprehensive approach provides practitioners with actionable guidance for implementing PPC technologies in digital business ecosystems.
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previous chapter ContextER: An Entropy-Based Contextualized Process Discovery Approach to Balance Interpretability and Complexity
next chapter Towards Architectural Coordination of Digital Twin Development in Urban Planning
Title
Privacy-Preserving Computing in the Music Industry
Authors
Yulu Wang
Charlotte van de Velde
Sabine Oechsner
Jaap Gordijn
Copyright Year
2026
Book
The Practice of Enterprise Modeling

Print ISBN: 978-3-032-12062-5

Electronic ISBN: 978-3-032-12063-2

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-12063-2_17
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